class_docstr: Simple feedforward neural network that simultaneously predicts all future values.
  
  Based on `gluonts.torch.model.simple_feedforward.SimpleFeedForwardEstimator <https://ts.gluon.ai/stable/api/gluonts/gluonts.torch.model.simple_feedforward.html>`_.
  See GluonTS documentation for additional hyperparameters.
  
  
  Other Parameters
  ----------------
  context_length : int, default = max(10, 2 * prediction_length)
      Number of time units that condition the predictions
  hidden_dimensions: List[int], default = [20, 20]
      Size of hidden layers in the feedforward network
  distr_output : gluonts.torch.distributions.DistributionOutput, default = StudentTOutput()
      Distribution to fit.
  batch_normalization : bool, default = False
      Whether to use batch normalization
  mean_scaling : bool, default = True
      Scale the network input by the data mean and the network output by its inverse
  max_epochs : int, default = 100
      Number of epochs the model will be trained for
  batch_size : int, default = 64
      Size of batches used during training
  predict_batch_size : int, default = 500
      Size of batches used during prediction.
  num_batches_per_epoch : int, default = 50
      Number of batches processed every epoch
  lr : float, default = 1e-3,
      Learning rate used during training
  trainer_kwargs : dict, optional
      Optional keyword arguments passed to ``lightning.Trainer``.
  early_stopping_patience : int or None, default = 20
      Early stop training if the validation loss doesn't improve for this many epochs.
code: def _get_estimator_class(self) -> Type[GluonTSEstimator]:
      from gluonts.torch.model.simple_feedforward import (
          SimpleFeedForwardEstimator,
      )
  
      return SimpleFeedForwardEstimator
  
imports: import logging
  from typing import Any, Dict, Type
  from gluonts.model.estimator import Estimator
  from autogluon.timeseries.models.gluonts.abstract_gluonts import AbstractGluonTSModel
  from autogluon.timeseries.utils.datetime import get_lags_for_frequency, get_seasonality, get_time_features_for_frequency
label_desc: Use image node_id 1 for calling the SimpleFeedForwardModel obj's underlying member method code with example usage: obj._get_estimator_class() and returns: SimpleFeedForwardEstimator